Second, one can leverage a hierarchical structure of some AI computation graphs. Figure REF  shows a hierarchy of nodes, layers, and blocks that form an AI model.
As AI model architecture designers define, combine, and connect computation units, AI computation graphs are partitioned into programming modules (i.e., methods, layers or blocks) that can be used for placing measurement probes. Since the programming modules represent a logical partition of a computation graph based on ad-hoc or exhaustive experimentations (i.e., the Neural Architecture Search problem [1]}), they can define initial placements of measurement probes and lower memory and computational requirements for utilization computations.
<FIGURE>